Key factors influencing tree planting decisions of households: A case study in Hoa Binh province

In this paper analysed the key factors influencing tree planting decision from local people in the Nam Nuong commune, Kim Boi dictrict.

KEY FACTORS INFLUENCING TREE PLANTING DECISIONS OF HOUSEHOLDS: A CASE STUDY IN HOA BINH PROVINCE

Le Dinh Hai 1 , Pham Thanh Huong 2

1,2 Vietnam National University of Forestry

SUMMARY

In coping with significant deforestation and forest degradation, currently in Kim Boi district, Hoa Binh province, and massive reforestation projects have been implemented However, when remarkable attempts and investments have been made in reforestation, interaction of household characteristics and socio-economic factors with smallscale tree planting decision are still little understood In this study, we survey 150 households (including 75 households with tree planting and 75 households without tree-planting) in Nuong Dam commune, Kim Boi district, Hoa Binh province The results of stepwise binary logistic regression analysis indicate that the factors, including: Accessibility to Plantation Sites, Forestland Area, Investment Capital, and Knowledge on Silviculture have a significant effect on household’s decision on tree planting in the study area The study results may provide the basis for proposing solutions to strengthen tree planting of households in the study area

Keywords: Households, influential factors, stepwise binary logistic regression, tree planting decision

I INTRODUCTION

Recent history reveals both that the

large-scale reforestation projects of the 20th century

have often been less successful than anticipated,

and that tree growing by smallholders - as an

alternative means to combat deforestation and

promote sustainable land use - has received

relatively little attention from the scientific and

development communities (Snelder and Lasco,

2008) Related studies have shown that

smallholder tree planting activity is influenced

by socioeconomic characteristics such as access

to land with secure land and tree tenure (Byron,

2001; Emtage and Suh, 2004; Sikor and Baggio,

2014; Tran Thi Mai Anh, 2015); suitable

management skills, knowledge and labour

force; interaction with peer farmers’ through

either social groups or cooperative

organizations (Sikor and Baggio, 2014; Tran

Thi Mai Anh, 2015); environmental factors

(Summers et al., 2004; Jagger et al., 2005; Tran

Thi Mai Anh, 2015); and access to markets

(Akinnifesi et al., 2006; Tchoundjeu et al.,

2006; Kallio et al., 2011; Tran Thi Mai Anh,

2015)

In Vietnam, 1.2 million households have

been allocated 4.46 million ha, 70% of which

is production forest land (Phuc and Nghi,

2014) Understanding the socioeconomic

factors and perceptions of smallholders related

to tree planting activities in Vietnam will be

valuable for informing and supporting related policy interventions The perceptions of local people examine their views on how they consider tree planting activity If the incentives and disincentives to tree planting activities are understood, it will be easier to improve participation of smallholders and increase benefits from tree planting In this paper, we analysed the key factors influencing tree planting decision from local people in the Nam Nuong commune, Kim Boi district, Hoa Binh province and provide suggestion in sustainable management of forest plantation

in the study area

II REASEARCH METHODOLOGY 2.1 The study area

Hoa Binh Province is located in the North

of Vietnam is the source of headwater and major tributaries that influence the lives of more than 808,200 people inside the province

It borders Son La and Phu Tho provinces to the northwest, Ha Noi city to the north and northeast, Ha Nam province to the southeast, Ninh Binh and Thanh Hoa provinces to the south Hoa Binh is a mountainous province located on the entrance of the Northwest region and is proud to be famous with

“Hoabinhian Culture” where human life is proven to existed here since 10,000 - 2,000 BCE The topography is combined by mountains and narrow valleys results in the

climate of this district is representative for

tropical monsoon, which is pretty cold and less

rain in winter but hot and rainy in summer The

annual temperature varies between 150C to

290C, depending on season Hoa Binh is in the

region has a high poverty rate and a low

standard of living of the population The

growth of GDP amounts to 11.8% during 2000

- 2010 The poverty rate was 31.31% in 2005,

and was 14% in 2010, but in 2011 the rate of

poverty has jumped again to 37.68%,

according to the new rate of poverty (Mai Lan

Phuong, 2011) They are a large variety of

ethnic groups, which has 15 ethnic

communities, and 63.4% is Muong ethnic

group The variety of both culture and

environment leads to diverse land-use systems

Kim Boi District, Hoa Binh Province was

chosen to be a case study because of the

following reasons Kim Boi is considered as the

district with the largest planted forest area in the

province The total natural area of Kim Boi

district is 54,950 ha, of which 40,562 ha is

forestry land (account for 73% of the district's

natural area), and production forest area

accounts for over 21,000 ha On average, Kim

Boi district has planted 1,000 - 2,000 ha of

forest annually, mainly production forests and

100 - 200 ha of fruit trees In 2014, the district has planted 2001 ha of forest increasing the forest cover to 49.3% In 2018, Kim Boi district plans to plant 1,700 hectares of new forest, mainly production forests and allocate over 37,000 hectares of forests for people to manage and protect

Nuong Dam is a commune with extremely difficult socio-economic conditions in Kim Boi district, Hoa Binh province Nuong Dam commune lies in the tropical monsoon climate, with two distinct seasons: rainy and dry season, average temperature: 23°C, average humidity: 60%, the average rainfall: 1,800

mm Land of Nuong Dam commune is typically with high fertility suitable for many crops With hundreds of thousands of hectares

of land including the adjoining plots, land in Nuong Dam commune can be used for various purposes, especially afforestation, industrial crops for the agro-forestry and industrial development The Nuong Dam commune covers an area of 35.66 km² (in 2016), with a population of 3,381 people in 1999; 4,058 people in 2016, and a population density of

114 persons/km²

Figure 1 Map of Nuong Dam commune, Kim Boi district, Hoa Binh province

Source: People Committee of Nuong Dam commune, 2016

Nuong Dam commnue was chosen to be a case

study because of the following reasons Firstly,

Nuong Dam commune is a large forested area of

Kim Boi district which is representative for

mountainous area, bounded with streams, rivers,

valleys, and limestone mountains Secondly, this

area also is a focal point of planting for

headwater which plays an important role for

protecting water resource of whole regions

2.2 Study method

In this study, we selected 150 households

for survey according to the criteria in table 1

The attributes of the selected households are summarised in table 1 The survey was based

on the conceptual model for assessing key factors affecting the tree planting decision of households (figure 2) The survey was conducted by using a questionnaire designed

to collect data on general household characteristics, factors influencing tree planting decision of households A copy of the questionnaire is available on request The questionnaire was administered face-to-face, usually the head of households

Figure 2 Factors influence tree planting decision of smallholder

Source: Tran, 2015

Kim Boi district has 27 communes and

aninternal town with population of 114,000

people (GSO 2016) We conducted a

household survey in one representative

communes namely Nam Nuong commune, in

which, 150 households including 75

households having decision of tree planting

and 75 households without decision for tree

planting Within 75 tree planting households,

we divided into 3 sub-group based on

household wealth ranking including 25 rich

households, 25 moderate households and 25 poor households On the other hand, among 75 households not tree planting, 25 households are classified as rich, 25 households are classified as moderate, and 25 households are classified as poor The interview design was followed by a stratified random sampling method to obtain representative strata by decision of tree planting and household wealth ranking

Table 1 Number of survey households in the study area

Households wealth ranking

Personal interviews were conducted in the

study area This method allows researchers the

opportunity to ask more questions, longer

questions, more detailed questions, more

open-ended questions, and more complicated or

technical questions Moreover, face-to-face

surveys also offer advantages in terms of data

quality (Manurung et al., 2008) The survey

was conducted from 1st August 2017 to 20th

August 2017

IBM SPSS Statistics 23 was used for data analysis Bivariate analysis was used to identify association between ‘Tree planting decisions by households’ (dependent variable) and factor (independent variable) (see Table 2 for a full list of variables included in the analysis) Factors found to

be significantly associated with an independent variable in the bivariate analyses (p < 0.05) were considered as candidates in stepwise binary logistic regressions with independent variables

Table 2 Description of variables

4 Forestland area (ha) Forest land area of each household

1; Medium, accessible by

Factors were entered into the stepwise

regressions if the significance of their

relationship with an independent variable was

p < 0.05 and removed from the stepwise

regression if the significance of their

relationship with an independent variable

became p ≥ 0.10 Factors were entered into the

stepwise regressions in order of their

correlation with a dependent variable, from

most strongly (highest Pearson’s correlation)

to least strongly correlated (lowest Pearson’s

correlation) (Brace et al., 2006; Ho, 2006) A

set of significant factors for a dependent variable was the result of the stepwise binary logistic regression Stepwise regression is an appropriate analysis for this study because there are many variables (12 independent variables) in the binary logistic regression model and we are interested in identifying a useful subset of the predictors

III RESULTS AND DISCUSSION

3.1 Descriptive statistics on surveyed households

In general, almost all of households surveyed are Muong ethnicity (88%) The

results in table 4 show that approximate 60%

of the respondents having good knowledge on

silviculture and roughly 40% of total

households admit that they have little or even

no knowledge on this field In addition, most

of interviewees said an extension officer from

government forestry program is very important

in training and educating communities on tree

planting practices The more the farmers

interact with them, the more likely it is for

them to gain knowledge on silvilcuture The fact that, ‘Knowledge about forestry program’ for those who did not have knowledge about forestry program was a quarter of who have

‘Knowledge about forestry program’ And the accessibility from accommodation to forestland area is easy and moderate account for 12.7% and 49.3%, respective The rest is a difficult accessibility accounted for 38%

Table 3 Relationship between independent variables and tree planting decision of households

Investment capital

Accessibility to plantation sites

Knowledge about forestry

program

Source: Household survey, 2017

Results from table 4 show that there are

only significant differences at 5% level in ‘Age

of household head’ and ‘Forest land area’

between households decided to planting trees and households decided not planting the trees

Table 4 Descriptive statistics of quantitative variable

(2 tailed)

Mean Std Dev Mean Std Dev

Source: Household survey, 2017

3.2 Key drivers influencing tree planting

decision of surveyed household

Direct stepwise binary logistic regression

was performed to assess the impact of a

number of factors on the likelihood that

households would report that they had a

decision of planting trees or not The model

contained four independent variables

(Forestland area, Investment Capital,

Accessibility to Plantation Sites, and

Knowledge on Silviculture) The full model containing all predictors was statistically significant, χ2(4, N = 150) = 93.74, p < 001, indicating that the model was able to distinguish between respondents who decided and did not decide tree planting The model as

a whole explained between 46.5% (Cox and Snell R squared) and 62.0% (Nagelkerke R squared) of the variance in the decision of tree planting, and correctly classified 86.0% of cases

Table 5 Model summary for key drivers affecting tree planting decision of surveyed households

Dependent variable: Tree planting decision by households

Omnibus Tests of Model Coefficients:

Model summary:

Note: *** p < 0.01, ** p < 0.05, * p < 0.10, NS Not significance (two-tailed tests)

Source: Household survey, 2017

As shown in table 6, four independent

variables (Forestland Area, Investment Capital,

Accessibility to Plantation Sites, and

Knowledge on Silviculture) were statistically

significant in distinguishing between

households decide or did not decide to plant

decide to plant trees were improved by about 5.025 times if Accessibility to Plantation Sites

of household decrease one level from “difficult level” to “easy level”, by about 3.452 times if household has ‘Knowledge on Silviculture’, by about 1.970 times if Investment Capital

Table 6 Determining importance of variables in the multiple linear regression model

Source: Household survey, 2017

Exp(B)adjusted in table 6 shows that

‘Knowledge on Silviculture’, ‘Forestland

Area’, ‘Investment Capital’ variables have a

positive influence on the tree planting decision

of local households, and ‘Accessibility to

Plantation Sites’ variable is negatively

influenced on tree planting decision of local

households in the study area Ordinal

influential factors are represented as following:

(1) Accessibility to Plantation Sites; (2)

Knowledge on Silviculture; (3) Forestland

Area; and (4) Investment Capital

3.3 Discussions and Policy Implication

3.3.1 Accessibility to plantation site

Accessibility to plantation site was found

to be significantly and negatively related to

tree planting decision of households Dupuy

and Mille (1993) indicated that accessibility

of the planted area is a parameter that cannot

be overlooked, for it is important only in

reforestation per se, but also in the follow-up

(tending, thinning, and wildfire protection,

etc.) and in taking out harvested products

Therefore, the improvement of infrastructure,

such as roads, as part of forest plantation

programs is important to success, particular

where plantation sites are isolated and the

improved infrastructure can assist

communities to reliably access tree planting

inputs and product markets Infrastructure

development is very expensive and not all

projects are able to fulfil fund it, therefore

lower-cost options for infrastructure

improvement are vital

3.3.2 Forestland area

Result of this study indicated that forestland

area was found to be significantly and positively related to tree planting decision of households Byron (2001), Kallio (2013) and Tran Thi Mai Anh (2015) found that tree planters were generally with more land, higher value of total assets and more active participation in tree planting than non-tree planters

3.3.3 Investment capital

Funding from self-investment was found to

be significantly and positively related to tree planting decision of households Byron (2001), Sikor and Baggio (2014), and Tran Thi Mai Anh (2015) found that better-off households are more likely to possess forestland, grow trees, and invest in plantations than poor ones

In addition, land plantations, and investment tend to be larger for the better-off than the poor Better-off households are in a better position to engage in tree plantations due to, among other factors, the institutional mechanisms differentiating household access

to land and finance Sandewall et al (2010) revealed that many poor farmers had received forest land through the Forest Land Allocation (FLA), but their possibility to benefit from plantations was limited They had usually received land late in the process of FLA, as they initially declined to become involved; their plantations were small and far away, which complicated management and protection; they had to harvest prematurely to secure the necessary cash flow, and they did not have the necessary finances to maintain the plantations There were very limited credit facilities Therefore, the forest administration

such as the Department of Forestry

Development and the Forest Protection

Stations at District level, mainly had

regulatory, supervisory and monitoring tasks

3.3.4 Knowledge of household head about

silviculture

Knowledge on silviculture had significantly

positive effects on tree planting decision of

households Salam et al (2000) and Tran Thi

Mai Anh (2015) indicatedclearly that farmers’

awareness of forestry extension programs is

slight, and the contribution of forestry workers

to motivate farmers to plant trees has been

negligible To maximize the potential of

homestead forestry, forestry professionals and

extension workers should broaden their

activities and work more closely with local

farmers They should disseminate technical

information to tree growers, supply quality

seedlings suitable for the area, provide

effective institutional support, and arrange for

efficient marketing facilities of the farm forest

products so that poor farmers can come

forward to enhance tree production and get

proper returns from production Therefore,

reforestation education, information or

awareness building campaigns also provide

market information, and marketing support for

timber and other forest products that can help

to increase the cash income of farmers, which

in turn can lead to better site management and

protection, and reduced erosion and landslide

risk (Le et al., 2014)

IV CONCLUSION

A number of biophysical, socio-economic,

institutional and management factors influence

tree planting decision of household in Kim Boi

district, Hoa Binh province Based on our

analysis we found that ‘Accessibility to

Plantation Sites’, ‘Knowledge on Silviculture’,

‘Forestland Area’, and ‘Investment Capital’

were among the most highly connected factors

influencing tree planting decision of

households in the study area Therefore focusing on performance indicators alone will not improve our understanding of why households decide to plant or not plant trees Therefore, it is essential to develop infrastructure that can help farmers to easily access of plantation sites, better access to credit, provide farmers with more agroforestry extension activities

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CÁC NHÂN TỐ ẢNH HƯỞNG ĐÁNG KỂ ĐẾN QUYẾT ĐỊNH TRỒNG RỪNG CỦA CÁC HỘ GIA ĐÌNH: NGHIÊN CỨU ĐIỂM TẠI TỈNH HÒA BÌNH

Lê Đình Hải 1 , Phạm Thanh Hương 2

1,2 Trường Đại học Lâm nghiệp

TÓM TẮT

Để ứng phó với sự mất rừng và suy giảm tài nguyên rừng nghiêm trọng, đã có nhiều dự án khôi phục rừng đã được triển khai trên địa bàn huyện Kim Bôi, tỉnh Hòa Bình Tuy nhiên, khi mà những nỗ lực và đầu tư đáng kể vào khôi phục rừng, thì sự tương tác giữa đặc điểm của hộ gia đình và các yếu tố kinh tế xã hội có liên quan đến trồng rừng qui mô hộ gia đình còn được biết đến một cách hạn chế Trong nghiên cứu này chúng tôi khảo sát 150 hộ gia đình (bao gồm 75 hộ trồng rừng và 75 hộ không trồng rừng) trên địa bàn xã Nuông Dăm, huyện Kim Bôi, tỉnh Hòa Bình Kết quả phân tích ứng dụng mô hình hồi qui Stepwise Binary Logistic Regression đã xác định được 4 yếu ảnh hưởng đáng kể đến quyết định trồng rừng của hộ gia đình trên địa bàn nghiên cứu, bao gồm: khả năng tiếp cận rừng trồng, diện tích đất lâm nghiệp, vốn đầu tư và kiến thức về kỹ thuật lâm sinh Kết quả nghiên cứu có thể làm cơ sở cho việc đề xuất các giải pháp làm tăng cường và mở rộng trồng rừng qui mô

hộ gia đình trên địa bàn nghiên cứu

Từ khóa: Hộ gia đình, mô hình hồi qui logit chọn từng bước (stepwise binary logistic regresion), nhân tố ảnh hưởng, quyết định trồng rừng